So here’s the dream. You sit down at the keyboard and load a file of the structure of your new drug target – you’ve discovered that inhibition of Whateverase II or a ligand for the Type IV Whazzat receptor would be a good candidate for modifying some disease. You type out a few commands, and your speedy, capable virtual screening program goes to work fitting useful conformations of all the molecules in your company’s collection into the active site of the protein. When it’s finished with that – it doesn’t take that long, you know – it will go on to the current commercially available set of small molecules and do the same for them. If you want more, it has a function to enumerate new structures that it has reason to believe would be potent hits. Come back in a little while and the whole list will be rank-ordered for you.
I guess I should stipulate that you’re also young, extremely well-paid, and ferociously good-looking, and that Stripebutt, your rainbow-colored pet unicorn, is looking over your shoulder and whinnying appreciatively while you get all this done. Because sometimes it looks like Stripey’s going to make an appearance before that software ever does, pesky unicorn droppings and all – we’ve been trying to realize something like this for decades, and anyone who tells you that we’re there is trying to sell you something.
Here’s more or less the state of the art, a current paper in JACS. The authors are looking at selectivities of compounds across the bromodomain enzymes, an area that’s gotten a lot of attention the last few years. It’s a good proving ground – there are a lot of proteins, they’re related in a number of different ways, and selectivity between them is bound to be important. They’re trying absolute binding free energy (ABFE) calculations, which will vacuum up all the spare processing capacity you might have, even more so (I believe) than the relative-binding free energy calculations discussed here. Protein conformations are taken from X-ray structures, removing only the crystallographic water molecules that clashed with the ligands coming in.
Running three ligands (RVX-OH, RVX-208, and bromosporine) across 22 different bromodomains gave a list of predicted affinities and selectivities. The fit is pretty good when compared to experimental calorimetry data (“All of the predicted binding free energies were within 2 kcal/mol of the ITC values, and about two-thirds were within 1 kcal/mol.”) Keep in mind, though, that 1.4 kcal/mol is a tenfold difference in binding affinity, so a bench chemist’s assessment of these predictions and a computational chemist’s might well differ. One of the key features of RVX-OH versus RVX-208 is that the latter has selectivity for the second BET bromodomain, and the calculated data do seem to point that way (although it’s not a strong signal that you might be inclined to bet on).
Bromosporine has affinities all across the bromodomain proteins, so it’s a different sort of test, and in this case the calculations were more scattered (“Roughly a third of the results were within 1 kcal/mol of the ITC values, and two-thirds within 2 kcal/mol. However, this left another third of the results being off by at least 2 kcal/mol, which corresponds to about a 30-fold error in the dissociation constant“. One big part of that might be that good X-ray structures are available for the RVX compounds, so a lot was already known about their poses, whereas no such data are around for any of the bromosporine/protein combinations.
The fit got better (although still rocky) when they went back in and reparameterized the sulfonamide group, but that’s an interesting point in itself. A sulfonamide is not an exotic functional group, but treating it computationally can be a real challenge (“We then focused on the parameters of the soft dihedrals present in the molecule, since such terms are known to have limited transferability across different molecules and might lead to inaccurate sampling of ligand conformations. In particular, chemical groups such as sulfonamides are especially challenging when considering that also quantum effects like the interaction of the nitrogen lone pair with antibonding orbitals involving the sulfur affect the torsional energy around the N−S bond“). As long as this is as hard as it is, it’s going to be similarly hard to get actionable predictions for a lot of molecules, and not just sulfonamides.
This paper is a good filled-glass test, then. Computational chemists will probably see said glass as half full or more, because this sort of thing really is better than we’ve been able to achieve with earlier approaches. Experimental medicinal chemists, though, may well be looking for Stripebutt the unicorn to gallop in, dangling the Answers To All Their Problems from his radiant mane. We really would like to be able to screen compounds computationally and not have to run all those assays, because that would mean that we don’t have to make so many compounds that simply don’t work. That’s what we all spend most of our time in the lab doing now, and the idea that there might be something better is very appealing. But we’re still listening for those unicorn hoofbeats in the distance. . .